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Full Description
This book explores the technological developments at various levels of abstraction, of the new paradigm of approximate computing. The authors describe in a single-source the state-of-the-art, covering the entire spectrum of research activities in approximate computing, bridging device, circuit, architecture, and system levels. Content includes tutorials, reviews and surveys of current theoretical/experimental results, design methodologies and applications developed in approximate computing for a wide scope of readership and specialists.
Serves as a single-source reference to state-of-the-art of approximate computing;
Covers broad range of topics, from circuits to applications;
Includes contributions by leading researchers, from academia and industry.
Contents
Part I Approximate Arithmetic and Circuits.- 1-Approximate Arithmetic Circuits: Design and Applications.- 2-An Automated Logic Level Framework for Approximate Modular Arithmetic Circuits.- 3-Approximate Multiplier Design for Energy Efficiency: From Circuit to Algorithm.- 4-Low-Precision Floating-Point Formats: From General-Purpose to Application-Specific.- 5-Spintronic Solutions for Approximate Computing.- 6-Majority Logic Based Approximate Multipliers for Error-Tolerant Applications.- Part II Design Automation and Test.- 7-Approximate Logic Synthesis for FPGA by Decomposition.- 8-Design Techniques for Approximate Realization of Data-Flow Graphs.- 9-Approximation on Data Flow Graph Execution for Energy Efficiency.- 10-Test and Reliability of Approximate Hardware.- Part III Security.- 11-SecurityVulnerabilities in Approximate Circuits.- 12-Voltage Overscaling Techniques for Security Applications.- 13-Approximate Computing for Cryptography.- 14-Towards Securing Approximate Computing Systems: Security Threats and Attack Mitigation.- Part IV Neural Networks and Machine Learning.- 15-Approximate Computing for Machine Learning Workloads: A circuits and systems perspective.- 16-Approximate Computing for Efficient Neural Network Computation.- 17-Enabling Efficient Inference of Convolutional Neural Networks via Approximation.- 18-Approximate Computing for Energy-Constrained DNN-based Speech Recognition.- 19-Efficient Approximate DNN Accelerators for Edge Devices.- Part V Applications.- 20-Cross-Level Design of Approximate Computing for Continuous Perception System.- 21-Approximate Computing in Image Compression and Denoising.- 22-Approximate Computation for Baseband Processing.
